def _test_appending(self, modified_input, expected_output):
project = ProjectInDirectory(self.tmpdir)
module_path = putfile(
project.path, "module.py",
read_data("appending_test_cases_module_initial.py"))
test_module_path = putfile(
project.path, "test_module.py",
read_data("appending_test_cases_output_initial.py"))
# Analyze the project with an existing test module.
inspect_project(project)
# Filesystem stat has resolution of 1 second, and we don't want to
# sleep in a test, so we just fake the original files creation time.
project["module"].created = 0
project["test_module"].created = 0
# Modify the application module and analyze it again.
putfile(project.path, "module.py", read_data(modified_input))
inspect_project(project)
# Regenerate the tests.
add_tests_to_project(project, [module_path], 'unittest')
project.save()
assert_length(project.get_modules(), 2)
result = read_file_contents(test_module_path)
expected_result = read_data(expected_output)
assert_equal_strings(expected_result, result)
def _test_appending(self, modified_input, expected_output):
project = ProjectInDirectory(self.tmpdir)
module_path = putfile(project.path, "module.py", read_data("appending_test_cases_module_initial.py"))
test_module_path = putfile(project.path, "test_module.py", read_data("appending_test_cases_output_initial.py"))
# Analyze the project with an existing test module.
inspect_project(project)
# Filesystem stat has resolution of 1 second, and we don't want to
# sleep in a test, so we just fake the original files creation time.
project["module"].created = 0
project["test_module"].created = 0
# Modify the application module and analyze it again.
putfile(project.path, "module.py", read_data(modified_input))
inspect_project(project)
# Regenerate the tests.
add_tests_to_project(project, [module_path], 'unittest')
project.save()
assert_length(project.get_modules(), 2)
result = read_file_contents(test_module_path)
expected_result = read_data(expected_output)
assert_equal_strings(expected_result, result)
def test_generates_test_stubs(self):
expected_result = read_data("static_analysis_output.py")
project = ProjectInDirectory(self.tmpdir)
module_path = putfile(project.path, "module.py", read_data("static_analysis_module.py"))
inspect_project(project)
add_tests_to_project(project, [module_path], 'unittest')
result = get_test_module_contents(project)
assert_equal_strings(expected_result, result)
def execute_with_point_of_entry_and_assert(self, id):
expected_result = read_data("%s_output.py" % id)
project = ProjectInDirectory(self.tmpdir).with_points_of_entry(["poe.py"])
module_path = putfile(project.path, "module.py", read_data("%s_module.py" % id))
write_content_to_file(read_data("generic_acceptance_poe.py"), project.path_for_point_of_entry("poe.py"))
inspect_project(project)
add_tests_to_project(project, [module_path], 'unittest')
result = get_test_module_contents(project)
assert_equal_strings(expected_result, result)
def test_generates_test_stubs(self):
expected_result = read_data("static_analysis_output.py")
project = ProjectInDirectory(self.tmpdir)
module_path = putfile(project.path, "module.py",
read_data("static_analysis_module.py"))
inspect_project(project)
add_tests_to_project(project, [module_path], 'unittest')
result = get_test_module_contents(project)
assert_equal_strings(expected_result, result)
def execute_with_point_of_entry_and_assert(self, id):
expected_result = read_data("%s_output.py" % id)
project = ProjectInDirectory(self.tmpdir).with_points_of_entry(
["poe.py"])
module_path = putfile(project.path, "module.py",
read_data("%s_module.py" % id))
write_content_to_file(read_data("generic_acceptance_poe.py"),
project.path_for_point_of_entry("poe.py"))
inspect_project(project)
add_tests_to_project(project, [module_path], 'unittest')
result = get_test_module_contents(project)
assert_equal_strings(expected_result, result)
def prepareData(file_path, n, K):
uic_lists = helper.read_data(file_path)
print('read 5000 users done...')
print('clustering users...This might take a minute...')
cu = ClusterUsers(uic_lists, n, K)
(centroids, groups) = cu.cluster()
return (uic_lists, centroids, groups)
def pre_pos():
training_data = helper.read_data('../asset/test_words.txt')
a = []
for i in training_data:
temp = word(i)
a.append(temp)
return a
def request_evaluation(attackDetails):
#Load the response system
responseSystem = read_data('data/response_system.json')
#Request for evaluation from the server
client.sock.sendall(
serialize('request_evaluation',
attackDetails + '!.!' + responseSystem[attackDetails]))
#Wait for the ACK from server
client.RESPONSE_ACK.wait()
def split():
line = helper.read_data("./asset/training_data.txt")
train_file = open("./asset/train.txt", 'w+')
test_file = open("./asset/test.txt", 'w+')
for i in line:
if random.randint(0, 9):
train_file.write(i + '\n')
else:
test_file.write(i + '\n')
def fit(self, sess):
saver = tf.train.Saver(max_to_keep=None)
check_dir = Helper.get_checkpoint_dir(self.p.name)
if self.p.restore:
save_path = os.path.join(check_dir, f"epoch_{self.p.restore_epoch}")
saver.restore(sess, save_path)
if not self.p.onlyTest:
print("Start fitting")
validation_data = Helper.read_data(self.p.valid_data, self.p.granularity)
for epoch in range(self.p.max_epochs):
loss = self.run_epoch(sess, self.data)
if epoch % 50 == 0:
print(f"Epoch {epoch}\t Loss {loss}\t model {self.p.name}")
if epoch % self.p.test_freq == 0 and epoch != 0:
## -- check pointing -- ##
save_path = os.path.join(check_dir, f"epoch_{epoch}")
saver.save(sess=sess, save_path=save_path)
print("Validation started")
if self.p.mode == "temporal":
Pred.temp_test_against(self, sess, validation_data, "valid", epoch)
elif self.p.mode == "entity":
Pred.test_link_against(self, sess, validation_data, "valid", epoch)
else:
raise ValueError
print("Validation ended")
else:
print("Testing started")
test_data = Helper.read_data(self.p.test_data, self.p.granularity)
if self.p.mode == "temporal":
Pred.temp_test_against(self, sess, test_data, "test", self.p.restore_epoch)
elif self.p.mode == "entity":
Pred.test_link_against(self, sess, test_data, "test", self.p.restore_epoch)
else:
raise ValueError
print("Testing ended")
def main():
data_set, n_neurons, iterations, k, plot_k, neighborhood, learning_rate\
, radius = get_input()
cities = read_data(data_set)
scaling, cities = normalize(cities)
neuron_count = len(cities) * n_neurons
neurons = init_neurons(neuron_count)
# TODO maybe distance param?
som(neurons, cities, iterations, k, plot_k, neighborhood, learning_rate,
radius, scaling)
def convert():
raw_data = helper.read_data('./asset/test.txt')
result = []
file = open("result.txt", "w+b")
for i in raw_data:
counter = 0
for char in i:
if char in '012':
counter += 1
if char == '1':
result.append(counter)
break
pickle.dump(result, file)
def response_system(attackDetails):
#Under attack state is being processed, clear it
UNDER_ATTACK.clear()
if not specifications['is_collaborative']:
#After detecting the attack -> request evaluation from the server
evaluationThread = threading.Thread(
target=request_evaluation(attackDetails))
evaluationThread.start()
#Wait for the thread to finish
evaluationThread.join()
#Clear the response ack
client.RESPONSE_ACK.clear()
#Load the response system
responseSystem = read_data('data/response_system.json')
responseTechnique = responseSystem[attackDetails]
techniquePath = responseTechniquesData[
responseTechnique] # The path for the technique script
running_response_technique(attackDetails, responseTechnique,
techniquePath)
else:
#Assign car to busy and save it locally
specifications['is_busy'] = True
save_data('data/specifications.json', specifications)
#Load the response system
responseSystem = read_data('data/response_system.json')
responseTechnique = responseSystem[attackDetails]
#Run the response technique applied by the response system
print('Under {} attack! Running {}!'.format(attackDetails,
responseTechnique))
techniquePath = responseTechniquesData[
responseTechnique] # The path for the technique script
#Generate a new thread and run the response technique script
responseThread = threading.Thread(target=running_response_technique(
attackDetails, responseTechnique, techniquePath))
responseThread.start()
#Wait for the thread to finish
responseThread.join()
response_performed(attackDetails, responseTechnique, techniquePath)
def response_performed(attackDetails, responseTechnique, techniquePath):
#Send a message to server letting it know that the response technique is performed
client.sock.send(
serialize('ack', '{}!.!{}'.format('response performed',
responseTechnique)))
#The client waits for the server to finish the effectiveness test attack
print('Waiting for the server to perform the effectiveness attack')
client.FINISH_TESTING_EFFECTIVENESS.wait()
#If both are true, that means the response technique failed to stop the attack
print('Server done with effectiveness attack')
if UNDER_ATTACK.is_set(): isEffective = False
elif not UNDER_ATTACK.is_set(): isEffective = True
#Clear flags
UNDER_ATTACK.clear()
client.FINISH_TESTING_EFFECTIVENESS.clear()
#Load the saved file
print('Loading the log file')
logFile = read_data('data/logs/{}/{}.json'.format(attackDetails,
responseTechnique))
#Add effective status to the json file
logFile['is_effective'] = isEffective
#Send the log so we don't keep the server waiting for log file, in case this was the last technique to be assessed
print('Sending the log file')
client.sock.send(
send_log(
'log', "{}!.!{}!.!{}!.!{}".format(
specifications['software_version'],
specifications['hardware_specifications'], attackDetails,
responseTechnique), logFile))
#After sending the log, delete the log file
try:
os.remove('data/logs/{}/{}.json'.format(attackDetails,
responseTechnique))
except:
print("Error while deleting file: ", logFile)
#Revert changes done when the response technique applied
print('Running revert operation')
subprocess.run(shlex.split("{} {}".format(techniquePath, "revert")))
print('Revert operation successful')
#After getting back to the original state, change busy status to false
specifications['is_busy'] = False
#Save changes
save_data('data/specifications.json', specifications)
#Inform the server that the car is no longer busy
print(
'Sending busy status to server. Setting it to false (the car is ready for more work)'
)
client.sock.send(
serialize('ack', '{}!.!{}'.format('client busy status', 'false')))
def main():
# args = sys.argv[1:]
#
# if not len(args):
# sys.exit()
#
# array = [*map(cast_to_numeric, args)]
file_name = sys.argv[1]
if not len(file_name):
sys.exit()
array = read_data(file_name)
output = selection_sort(array)
print(' '.join(str(elem) for elem in output))
def evaluate_techniques(self, responseTechniques):
print("Evaluating techniques...")
#If one technique is not evaluated, then start the evaluation process
isEvaluationRequired = False
for technique in responseTechniques:
if not responseTechniques[technique]['is_evaluated']:
isEvaluationRequired = True
break
#Otherwise, no need for evaluation
if not isEvaluationRequired : return
#Update response techniques database
import glob
src = "data/logs/{}/{}/{}/".format(self.softwareVersion, self.hardwareSpecifications, self.attackDetails)
files = glob.glob('{}/*'.format(src), recursive=False)
# Loop through files
for single_file in files:
json_file = read_data(single_file)
fileName = os.path.basename(single_file)
techniqueName = fileName.replace('.json', '')
isEffective = json_file['is_effective']
responseTechniquesData[self.softwareVersion][self.hardwareSpecifications][self.attackDetails][techniqueName]['is_effective'] = isEffective
#Add every effective technique and its duration
effectiveTechniques = {}
for technique in responseTechniques:
if responseTechniques[technique]['is_effective']:
effectiveTechniques[technique] = responseTechniques[technique]['duration']
#Get the lowest duration technique
bestResponseTechnique = min(effectiveTechniques, key=effectiveTechniques.get)
print("Effective techniques are: ", effectiveTechniques)
print("Best effective response technique is {}.".format(bestResponseTechnique))
#Modify as necessary
responseTechniquesData[self.softwareVersion][self.hardwareSpecifications][self.attackDetails][bestResponseTechnique]['is_most_efficient'] = True
responseTechniquesData[self.softwareVersion][self.hardwareSpecifications][self.attackDetails][bestResponseTechnique]['is_evaluated'] = True
for technique in responseTechniques:
if not technique == bestResponseTechnique:
responseTechniquesData[self.softwareVersion][self.hardwareSpecifications][self.attackDetails][technique]['is_most_efficient'] = False
responseTechniquesData[self.softwareVersion][self.hardwareSpecifications][self.attackDetails][technique]['is_evaluated'] = True
#Save changes to the json file
save_data('data/response_techniques_database.json', responseTechniquesData)
def check_undergoing_assessment():
#First off, check if the car status is busy to see if there are unsent log files
if specifications['is_busy']:
print('The car is in busy status! Checking previous logs...')
#If still busy, then look for an un-sent log to the server. This happens when the client reboots, for example
#Get folder names (attack names, basically)
attackList = os.listdir('data/logs/')
#Loop through each subdirectory (attack) and send the response log
import glob
for attackName in attackList:
src = "data/logs/{}/*.json".format(attackName)
files = glob.glob(src, recursive=False)
# Loop through files
for single_file in files:
json_file = read_data(single_file)
fileName = os.path.basename(single_file)
techniqueName = fileName.replace('.json', '')
#Continue after performing the response
print('running response performed method now!')
response_performed(attackName, techniqueName, single_file)
break
def main():
# Read in data
os.chdir(
"/Users/MichaelChoie/Desktop/Data Science/deep-learning/embeddings")
text = helper.read_data()
# Process data
words = helper.preprocess(text)
vocab_to_int, int_to_vocab = helper.create_lookup_table(words)
int_words = [vocab_to_int[word] for word in words]
train_words = helper.subsampling(int_words)
# Create directory for model checkpoints
if not os.path.exists("checkpoints"):
os.mkdirs("checkpoints")
# Build computational graph
embed_mat = build_graph(train_graph, int_to_vocab, train_words)
# Visualize network
visualize(embed_mat)
from pyro.optim import Adam
from helper import read_data, get_train_test_seqs
import model
sequence_length = 50
engines_eval = [1, 2]
cuda = True
ftype = torch.cuda.FloatTensor if cuda else torch.FloatTensor
ltype = torch.cuda.LongTensor if cuda else torch.LongTensor
sensor_cols = ['s' + str(i) for i in range(1, 22)]
sequence_cols = ['cycle', 'setting1', 'setting2', 'setting3', 'cycle_norm']
sequence_cols.extend(sensor_cols)
train_df, test_df = read_data()
trainX, trainY, valX, valY, testX, testY = get_train_test_seqs(
train_df, test_df, sequence_length)
engines = []
for engine_id in engines_eval:
engines.append([])
train_one_eng = train_df[train_df.id == engine_id]
for i in range(train_one_eng.shape[0]):
engines[-1].append(train_one_eng[sequence_cols].values[:i])
sensor_cols = ['s' + str(i) for i in range(1, 22)]
sequence_cols = ['cycle', 'setting1', 'setting2', 'setting3', 'cycle_norm']
sequence_cols.extend(sensor_cols)
trainX = np.vstack([trainX, valX])
## import modules here
import helper
import submission
from sklearn.metrics import f1_score
from sklearn import model_selection
import matplotlib.pyplot as plt
training_data = helper.read_data('./asset/training_data.txt')
classifier_path = './asset/classifier.dat'
Y = submission.train(training_data, classifier_path)
test_str1 = './asset/tiny_test.txt'
test_str2 = './asset/testing_data1.txt'
test_data = helper.read_data(test_str1)
prediction = submission.test(test_data, classifier_path)
ground_truth = [1,1,2,1]
print(f1_score(ground_truth, prediction, average='micro'))
#k_range = range(1, 15)
#k_scores = []
#for k in k_range:
# knn = KNeighborsClassifier(n_neighbors=k)
# scores = model_selection.cross_val_score(knn, X, Y, cv=5, scoring='accuracy')
# print("k=",k," scores are", scores)
# k_scores.append(scores.mean())
#plt.plot(k_range, k_scores)
#plt.xlabel('Value of K for KNN')
#plt.ylabel('Cross-Validated Accuracy')
import helper
import submission
training_data = helper.read_data('./asset/training_data.txt')
test_data = helper.read_data('./asset/test_words.txt')
classifier_path = './asset/Logistic.dat'
submission.train(training_data, classifier_path, multi=True, DEBUG=True)
submission.test(test_data, classifier_path, DEBUG=True)
def draw(data):
result = []
for i in data:
if i[-1][0] == 0:
pre = -1
else:
pre = i[i[-1][0] -1]
if i[-1][0] == len(i) - 1:
suff = -1
else:
suff = i[i[-1][0] +1]
result.append((pre, suff))
return result
ab = helper.read_data("./asset/training_data.txt")
b, vasual = pre_process(ab)
seprate_point = math.ceil(len(b)*0.8)
tran_data = b[:seprate_point]
test_data = b[seprate_point:]
# visual_data = {}
# for i in tran_data:
# if i[-1][1] in visual_data:
# visual_data[i[-1][1]].append(i)
# else:
# visual_data[i[-1][1]] = [i]
#
# for i in visual_data:
# data = draw(i)
# data_fram = pd.DataFrame(data,index=['pre', 'next'])
import helper
import submission
from sklearn.metrics import f1_score
if __name__ == '__main__':
training_data = helper.read_data('./asset/training_data.txt')
classifier_path = './asset/classifier.dat'
submission.train(training_data, classifier_path)
test_data = helper.read_data('./asset/tiny_test.txt')
prediction = submission.test(test_data, classifier_path)
print(prediction)
ground_truth = [1, 1, 2, 1]
print(f1_score(ground_truth, prediction, average='micro'))
def popular_artists():
df = pd.read_csv('sentiment_data/filtered_grand_df.csv')
df_normalized = read_data(df)
chart_1 = sentiment_plot(df_normalized, 0.15)
script_1, div_1 = components(chart_1)
polarity = df.groupby('release_year')[['polarity']].mean().reset_index()
polarity['text'] = round(polarity['polarity'], 2)
chart_2 = polarity_plot(polarity, 0.1, 0.45)
script_2, div_2 = components(chart_2)
df_2000, df_2001, df_2002, df_2003, df_2004, df_2005, df_2006, df_2007, df_2008, df_2009, df_2010, df_2011, df_2012, df_2013, df_2014, df_2015, df_2016, df_2017, df_2018, df_2019 = topics_per_year(
)
artists = df.groupby('primary_artist')[[
'anger', 'anticipation', 'disgust', 'fear', 'joy', 'positive',
'negative', 'sadness', 'surprise', 'trust'
]].mean()
artists = artists.reset_index()
clusters = cluster_data(artists)
clusters['artist'] = artists['primary_artist']
chart_3 = artists_cluster(clusters)
script_3, div_3 = components(chart_3)
return render_template('popular_artists.html',
the_script_1=script_1,
the_div_1=div_1,
songs_no=df.shape[0],
the_script_2=script_2,
the_div_2=div_2,
topic_1=df_2000['words'][0],
topic_2=df_2000['words'][1],
topic_3=df_2000['words'][2],
topic_4=df_2001['words'][0],
topic_5=df_2001['words'][1],
topic_6=df_2001['words'][2],
topic_7=df_2002['words'][0],
topic_8=df_2002['words'][1],
topic_9=df_2002['words'][2],
topic_10=df_2003['words'][0],
topic_11=df_2003['words'][1],
topic_12=df_2003['words'][2],
topic_13=df_2004['words'][0],
topic_14=df_2004['words'][1],
topic_15=df_2004['words'][2],
topic_16=df_2005['words'][0],
topic_17=df_2005['words'][1],
topic_18=df_2005['words'][2],
topic_19=df_2006['words'][0],
topic_20=df_2006['words'][1],
topic_21=df_2006['words'][2],
topic_22=df_2007['words'][0],
topic_23=df_2007['words'][1],
topic_24=df_2007['words'][2],
topic_25=df_2008['words'][0],
topic_26=df_2008['words'][1],
topic_27=df_2008['words'][2],
topic_28=df_2009['words'][0],
topic_29=df_2009['words'][1],
topic_30=df_2009['words'][2],
topic_31=df_2010['words'][0],
topic_32=df_2010['words'][1],
topic_33=df_2010['words'][2],
topic_34=df_2011['words'][0],
topic_35=df_2011['words'][1],
topic_36=df_2011['words'][2],
topic_37=df_2012['words'][0],
topic_38=df_2012['words'][1],
topic_39=df_2012['words'][2],
topic_40=df_2013['words'][0],
topic_41=df_2013['words'][1],
topic_42=df_2013['words'][2],
topic_43=df_2014['words'][0],
topic_44=df_2014['words'][1],
topic_45=df_2014['words'][2],
topic_46=df_2015['words'][0],
topic_47=df_2015['words'][1],
topic_48=df_2015['words'][2],
topic_49=df_2016['words'][0],
topic_50=df_2016['words'][1],
topic_51=df_2016['words'][2],
topic_52=df_2017['words'][0],
topic_53=df_2017['words'][1],
topic_54=df_2017['words'][2],
topic_55=df_2018['words'][0],
topic_56=df_2018['words'][1],
topic_57=df_2018['words'][2],
topic_58=df_2019['words'][0],
topic_59=df_2019['words'][1],
topic_60=df_2019['words'][2],
the_script_3=script_3,
the_div_3=div_3)
def compare_artists():
try:
artist_1 = flask.request.args['name_1']
artist_2 = flask.request.args['name_2']
stripped_artist_1 = artist_1.lower().replace(' ', '').replace(
'&', '').replace('é', 'e')
stripped_artist_2 = artist_2.lower().replace(' ', '').replace(
'&', '').replace('é', 'e')
df_1 = pd.read_csv(f'sentiment_data/{stripped_artist_1}.csv')
df_2 = pd.read_csv(f'sentiment_data/{stripped_artist_2}.csv')
normalized_df_1 = read_data(df_1)[[
'anger', 'positive', 'negative', 'anticipation', 'disgust', 'fear',
'joy', 'sadness', 'surprise', 'trust'
]].mean()
normalized_df_2 = read_data(df_2)[[
'anger', 'positive', 'negative', 'anticipation', 'disgust', 'fear',
'joy', 'sadness', 'surprise', 'trust'
]].mean()
anger = (normalized_df_1['anger'] -
normalized_df_2['anger']) / normalized_df_1['anger'] * 100
anticipation = (normalized_df_1['anticipation'] -
normalized_df_2['anticipation']
) / normalized_df_1['anticipation'] * 100
disgust = (normalized_df_1['disgust'] - normalized_df_2['disgust']
) / normalized_df_1['disgust'] * 100
fear = (normalized_df_1['fear'] -
normalized_df_2['fear']) / normalized_df_1['fear'] * 100
joy = (normalized_df_1['joy'] -
normalized_df_2['joy']) / normalized_df_1['joy'] * 100
sadness = (normalized_df_1['sadness'] - normalized_df_2['sadness']
) / normalized_df_1['sadness'] * 100
surprise = (normalized_df_1['surprise'] - normalized_df_2['surprise']
) / normalized_df_1['surprise'] * 100
trust = (normalized_df_1['trust'] -
normalized_df_2['trust']) / normalized_df_1['trust'] * 100
positivity = (normalized_df_1['positive'] - normalized_df_2['positive']
) / normalized_df_1['positive'] * 100
topics_1 = pd.read_csv(f'topics_data/{stripped_artist_1}.csv',
index_col=0)
topics_2 = pd.read_csv(f'topics_data/{stripped_artist_2}.csv',
index_col=0)
most_frequent_words_1 = most_frequent_words(df_1)
most_frequent_words_2 = most_frequent_words(df_2)
return render_template('compare_artists.html',
artist_1=artist_1.upper(),
artist_2=artist_2.upper(),
artist_1_proper=artist_1,
artist_2_proper=artist_2,
positivity=round(positivity, 2),
anger=round(anger, 2),
anticipation=round(anticipation, 2),
disgust=round(disgust, 2),
fear=round(fear, 2),
joy=round(joy, 2),
sadness=round(sadness, 2),
surprise=round(surprise, 2),
trust=round(trust, 2),
topic_1=topics_1['words'][0],
topic_2=topics_1['words'][1],
topic_3=topics_1['words'][3],
topic_4=topics_2['words'][0],
topic_5=topics_2['words'][1],
topic_6=topics_2['words'][2],
freq_word_1=most_frequent_words_1[0],
freq_word_2=most_frequent_words_1[1],
freq_word_3=most_frequent_words_1[2],
freq_word_4=most_frequent_words_1[3],
freq_word_5=most_frequent_words_1[4],
freq_word_6=most_frequent_words_2[0],
freq_word_7=most_frequent_words_2[1],
freq_word_8=most_frequent_words_2[2],
freq_word_9=most_frequent_words_2[3],
freq_word_10=most_frequent_words_2[4])
except:
return render_template('inventory_error.html')
dataset = 'ShapeNet'
category = 'airplane'
mode = 'test'
root = '/disk1/yicheng/ShapeNet'
dump_root = '/disk1/yicheng/' + dataset + '_csk/'
if dataset not in ['ModelNet10', 'ShapeNet', 'dfaust', 'faces', 'sunrgbd']:
raise Exception('dataset error.')
if dataset == 'ShapeNet':
data_dir = root + mode + '_data_npy/' + category + '/'
list_el = glob.glob(os.path.join(data_dir, '*.npy'))
elif dataset == 'ModelNet10':
data_dir = root + category + '/' + mode + '/'
list_el = glob.glob(os.path.join(data_dir, '*.off'))
dump_dir = dump_root + mode + '_data_npy/' + category + '/'
if not os.path.exists(dump_dir):
os.makedirs(dump_dir)
for i, name in enumerate(list_el):
pc = read_data(name, dataset)
pc = normalize_data(pc)
file_name = Path(name).stem
np.save(os.path.join(dump_dir, file_name + '.npy'), pc)
print('done!')
def artist():
try:
artist = flask.request.args['name']
stripped_artist = artist.replace(' ',
'').replace('&',
'').replace('é', 'e')
df = pd.read_csv(f'sentiment_data/{stripped_artist}.csv', index_col=0)
df = df.drop_duplicates()
polarity = round(df.polarity.mean(), 2)
df['release_date'] = pd.to_datetime(df['release_date'],
errors='coerce')
df['release_year'] = df['release_date'].dt.year
polarity_graph = df.groupby('release_year')[['polarity'
]].mean().reset_index()
polarity_graph['text'] = round(polarity_graph['polarity'], 2)
chart_4 = polarity_plot(polarity_graph, -1.25, +2)
normalized_df = read_data(df)
chart_1 = sentiment_plot(normalized_df, 0.4)
ts = total_sentiments(df)
anger = round(float(ts.anger) * 100, 2)
anticipation = round(float(ts.anticipation) * 100, 2)
sadness = round(float(ts.sadness) * 100, 2)
joy = round(float(ts.joy) * 100, 2)
surprise = round(float(ts.surprise) * 100, 2)
trust = round(float(ts.trust) * 100, 2)
fear = round(float(ts.fear) * 100, 2)
disgust = round(float(ts.disgust) * 100, 2)
clusters = cluster_data(df)
clusters['title'] = df['title']
clusters['album'] = df['album']
clusters = clusters.replace('None', 'Unknown')
chart_2 = cluster_plot(clusters)
albums = albums_data(df)
chart_3 = view_albums(albums)
script_1, div_1 = components(chart_1)
script_2, div_2 = components(chart_2)
script_3, div_3 = components(chart_3)
script_4, div_4 = components(chart_4)
image = f'static/images/word_clouds/{stripped_artist}.png'
data_url = f'/artist_data?name={stripped_artist}'
topics = pd.read_csv(f'topics_data/{stripped_artist}.csv', index_col=0)
return render_template('artist.html',
the_script_1=script_1,
the_div_1=div_1,
the_script_2=script_2,
the_div_2=div_2,
polarity=polarity,
artist=artist.title(),
image=image,
the_script_3=script_3,
the_div_3=div_3,
data_url=data_url,
topic_1=topics['words'][0],
topic_2=topics['words'][1],
topic_3=topics['words'][2],
topic_4=topics['words'][3],
topic_5=topics['words'][4],
the_script_4=script_4,
the_div_4=div_4,
anger=anger,
anticipation=anticipation,
fear=fear,
joy=joy,
sadness=sadness,
trust=trust,
surprise=surprise,
disgust=disgust)
except:
return render_template('inventory_error.html')
def update_and_attack(onRoadCar, attackDetails, responseTechnique):
#Check for non busy collaborative cars with the same software version and hardware specs
print("Checking for non-busy collaborative cars")
car = find_car(onRoadCar)
print("Car ID: {} will start assessing: {} now!".format(car.vin, responseTechnique))
#Event to wait for an ACK message regarding the update
car.UPDATE_ACK = threading.Event()
print("Sending update...")
print("-------------------------")
#Send update with the response technique to use
car.socket.send(serialize('update', attackDetails + " " + responseTechnique))
#Wait for the car to ACK
car.UPDATE_ACK.wait()
#Clear the ACK flag
car.UPDATE_ACK.clear()
print("Update applied successfully!")
print("-------------------------")
#Create and add the task into the work list
task = WorkTask(car, attackDetails, responseTechnique)
workList.append(task)
print("Simulating {} attack on car: {} using technique: {}".format(attackDetails, car.vin, responseTechnique))
print("*************************")
#Start the attack simulation process
attackData = read_data('data/attack_simulations.json')
attackPath = attackData[attackDetails]
#Run the attack script
print('car address is: {}'.format(car.address[0]))
print(type(car.address[0]))
print('attack path is: {}'.format(attackPath))
subprocess.run(shlex.split("{} {}".format(attackPath, car.address[0])))
#After initiating the attack, measure the duration
task.startTime = time.time()
print("start: ", task.startTime)
#Wait for effectiveness ACK from client showing that the client has performed the response technique and waiting for the effectiveness test
task.EFFECTIVENESS_ACK.wait()
#Mark the elapsed time
task.elapsedTime = time.time() - task.startTime
#Re-initiate the attack simulation again to check for effectiveness
#Keep in mind that the car might get disconnected, so rely on the VIN instead
#Run the attack script
print('Re-initiating the attack to test for effectiveness...')
subprocess.run(shlex.split("{} {}".format(attackPath, task.car.address[0])))
#After running the attack script, send a message to let the client know that the server has finished attacking
task.car.socket.send(serialize('ack', 'finish testing effectiveness'))
#Wait for ACK on receiving the log file
print("Waiting for the log file...")
task.LOG_ACK.wait()
#Update the technique's data
responseTechniquesData[task.car.softwareVersion][task.car.hardwareSpecifications][attackDetails][responseTechnique]['is_assessed'] = True
responseTechniquesData[task.car.softwareVersion][task.car.hardwareSpecifications][attackDetails][responseTechnique]['duration'] = task.elapsedTime
#Remove task from the work list
workList.remove(task)
#Check if this is the last task
if not workList:
ASSESSMENT_FLAG.set()
This module use cross_validation to check the f1 of our classifier
in order to get a better classifier to the predict problem.
"""
import helper
from submission import *
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.metrics import f1_score
if __name__ == '__main__':
# 先把训练集读进来，然后弄出特征矩阵和对应的label数组
# 然后用train_test_split来在训练集里面分出训练集和测试集，（这个过程只用到特征矩阵和label数组，与原来的数据字符串没有关系了）
raw_data = helper.read_data('./asset/training_data.txt')
# 在这里提取出想要的特征矩阵features和对应的分类数组labels数组
features, labels = training_preprocess(raw_data)
clf = get_selected_classifier()
# 模式选择，输入y表示使用均值模拟，多次交叉检验，减少误差
# 输入其他表示不使用均值模拟，减少运行时间
print("Do you want to test for multiple times? y/n [default:n]")
# choice = input()
choice = 'y'
if choice != 'y':
x_train, x_test, y_train, y_test = train_test_split(features,
labels,
import maxSum as ms
import helper as hp
for i in range(1,4):
print 'test '+str(i)+':'
matrix,m,n = hp.read_data('../data/test'+str(i)+'.txt')
print 'finish reading test data'
T = ms.maxSum(matrix,m,n)
print 'finish computing T'
a,b,score = ms.findMaxScore(T,m,n)
print 'finish finding max score'
stack = ms.backTrack(T,matrix,a,b)
print 'finish backTrack'
output = hp.write_data(stack,score,'../data/test'+str(i)+'grp12.txt')
print 'finish writing output'
tgt_results = []
pbar = ProgressBar()
for i in pbar(range(len(orig_data))):
en_point = i - batch_size
de_point = i + batch_size
if en_point < 0:
en_point = 0
if de_point > len(simi_src_data) - 1:
de_point = len(simi_src_data) - 1
max_bleu, bleu[0], bleu[1] = get_max_bleu(
orig_data[i], simi_src_data[en_point:de_point])
tgt_results.append(' '.join(
simi_tar_data[(en_point + bleu[0]):(en_point + bleu[1])]))
print_result(orig_data, tgt_results)
return tgt_results
helper.log_w("Reading data. Please wait...")
orig_data = helper.read_data(ORIG_FILE)
simi_src_data = helper.read_data(SIMI_FILE_SRC_LANG)
simi_tar_data = helper.read_data(SIMI_FILE_TAR_LANG)
helper.log_w("Compute similarity...")
out_tgt_data = similarize(orig_data, simi_src_data, simi_tar_data, 10)
helper.log_w("Writing new data...")
helper.write_data(DEST_FILE, out_tgt_data)
helper.log_w("Done.")
from helper import parse_args, read_data
import numpy as np
def predict(X, W):
return np.sign(np.sum(np.multiply(X, W)))
if __name__ == '__main__':
input, output = parse_args()
# read training data
X, Y = read_data(input)
n, d = X.shape # number of training examples and features
# initialize weights with 0
W = np.zeros(d)
result = np.empty(shape=(0, d), dtype=np.int)
# repeat until convergence
while True:
converged = True
for i in range(n):
x, y = X[i], Y[i]
y_pred = predict(x, W)
if y * y_pred <= 0: # misclassified?
converged = False
for j in range(d):
import helper as h
import numpy as np
data = h.read_data()
print data
def output_layer_fp(Input, W, b):
# W shape: (1, input_size)
# b shape: (1, 1)
output = np.matmul(Input, W.T) + b
return h.sigmoid(output)
def hidden_layer_fp(Input, W, b):
# W shape: (hidden_size, feature_size)
# b shape: (1, hidden_size)
# Task 1: Perform sum(xi*wi) + b for every input value
output = # TODO: use np.dot or np.matmul function
return # TODO: Use the tanh function from the numpy package
# Task 2: Prepare the network parameters
X = data.as_matrix(['x1', 'x2'])
hidden_size = 10
W1 = # TODO: Create the (hidden_size, feature_size) array of weights
b1 = # TODO: Set the value of the bias
W2 = # TODO: Create the (1, hidden_size) array of weights
b2 = # TODO: Set the value of the bias
def apply_update(attackDetails, responseTechnique):
print('Update received! Applying: {} for: {}'.format(responseTechnique, attackDetails))
#Read and update the response_system file
responseSystem = read_data('data/response_system.json')
responseSystem[attackDetails] = responseTechnique
save_data('data/response_system.json', responseSystem)
